####Section 7 Feature Selection ####
library(ISLR)
library(leaps) # contains regsubsets()
#查看哪些变量重要，那些变量不需要
####Exercise 1: best subset selection ####
rm(list=ls())
Hitters = na.omit(Hitters)
dim(Hitters)
# (1) 执行最佳子集选择
reg.full = regsubsets(Salary~., data=Hitters, method="exhaustive")
# method=c("exhaustive","backward", "forward", "seqrep"),
# method="exhaustive"穷举 是最优的
#（2）解释标准 regsubsets() 输出。
names(summary(reg.full))
summary(reg.full) #有*表示要这个变量；没*就不要这个变量
summary(reg.full)$which # 追踪穷举过程
summary(reg.full)$outmat # 追踪穷举过程
#举例最后的结果是：
#y~x1+x5
#y~x2+x3+x4
# (3)将 RSS 绘制为变量数量的函数
RSS = summary(reg.full)$rss
plot(RSS, pch=20, t='b', xlab="Number of covariates", ylab='RSS')#残差平方和
# 随着Number of covariates的增大，RSS依旧不能趋于稳定，那么我们要换adjusted R square调整后的 r 平方作为判断标准
R2adj = summary(reg.full)$adjr2 #调整后的 r 平方
plot(R2adj, pch=20, t='b', xlab="Number of covariates",
ylab='Adjusted R^2')
#r 相关系数
#增加自变量的个数时，判定系数就会增加，
#即随着自变量的增多，R平方会越来越大，
#会显得回归模型精度很高，有较好的拟合效果。而实际上可能并非如此，
#有些自变量与因变量（即预测）完全不相关，
#增加这些自变量，并不会提升拟合水平和预测精度。为避免这种现象
#因此需要调整后的R平方
# （4）将最终模型的所需大小增加到 19，并找出哪个子模型有：
#最小的 RSS
#最高调整后的 R2
reg.full = regsubsets(Salary~., data=Hitters, nvmax=19)
par(mfrow=c(1,2))
RSS = summary(reg.full)$rss
plot(RSS, pch=20, t='b', xlab="Number of covariates", ylab='RSS')
#到10的时候才开始趋于稳定，RSS不好
R2 = summary(reg.full)$rsq #调整前的 r 平方
R2adj = summary(reg.full)$adjr2 #调整后的 r 平方
plot(R2, pch=20, t='b', xlab="Number of covariates",
ylab='Original and adjusted R^2')
points(R2adj, col=4, pch=15, t='b')
# 找到最优值，也就是最好有几个变量
R2adj.index = which.max(R2adj)
# 把这个值画在图里
abline(v = R2adj.index)
#11个变量
#提取找到对应的模型：
summary(reg.full)$outmat[R2adj.index,]
mod = summary(reg.full)$which[R2adj.index,]#提取需要的
names(which(mod))
# 画对应的heatmap:
plot(reg.full, scale="adjr2")
summary(reg.full)$outmat
# 与上面的追踪相对应
#0.5为调整后的R平方的临界值，
#如果调整后的R平方小于0.5，则要分析我们所采用和未采用的自变量。
#如果调整后的R平方与R平方存在明显差异，
#则意味着所用的自变量不能很好的测算因变量的变化，
#或者是遗漏了一些可用的自变量
#调整后的R平方与R平方间差距越大，模型的拟合越差。
#### Exercise 2: fwd/bwd subset selection ####
#1. 使用 jumps::regsubsets() 执行向前和向后选择。
#2. 比较 RSS 和 Adjusted R2 w.r.t 的图。 模型尺寸。
#3. 比较通过每种方法选择的 4 变量模型中的协变量。
#4. 以任何其他方式探索最终的模型选择
rm(list=ls())
Hitters = na.omit(Hitters)
dim(Hitters)
#（1）使用两种方法
reg.fwd = regsubsets(Salary~., data=Hitters, nvmax=19, method="forward")
reg.bwd = regsubsets(Salary~., data=Hitters, nvmax=19, method="backward")
#（2）比较
par(mfrow=c(1,2))
#画rss图
plot(summary(reg.bwd)$rss, t='b', pch=20, cex=1.5)
points(summary(reg.fwd)$rss, t='b', pch=15, col=2)
#画adjr2图
plot(summary(reg.bwd)$adjr2, t='b', pch=20, cex=1.5)
points(summary(reg.fwd)$adjr2, t='b', pch=15, col=2)
#最优值
R2adj.fwd.index = which.max(summary(reg.fwd)$adjr2)
R2adj.bwd.index = which.max(summary(reg.bwd)$adjr2)
abline(v=c(R2adj.bwd.index, R2adj.fwd.index), col=c(1,2))
#（3）找每个模型中有4个变量的模型
# 4-variable models:
#id:变量个数
coef(reg.fwd, id=4)
coef(reg.bwd, id=4)
# 但最好的还是"exhaustive"
reg.full = regsubsets(Salary~., data=Hitters, method="exhaustive")
names(which(summary(reg.full)$which[4,]==TRUE))
# 从反向消除过程中提取最优模型：
coef(reg.bwd, id=R2adj.bwd.index)
#### Exercise 3: generate predictions from regsubsets() output?####
#但是在Exercise 5里，加了两行代码就能用了
rm(list=ls())
dat = na.omit(Hitters)
n = nrow(dat)
itrain = sample(1:n, 150)
reg.fwd = regsubsets(Salary~., data=dat, nvmax=10,
method="forward", subset=itrain)
#predict(reg.fwd, newdata=dat[-itrain,])
#predict不能用，得自己写predict
beta.hat = coef(reg.fwd, id=4)
# create matrix X:
test.dat = model.matrix(Salary~., data = dat[-itrain,])
# get the test data matrix:
Xtest = test.dat[,names(beta.hat)]
# compute (X Beta^T) as in Y = (X Beta^t) + Epsilon:
pred = Xtest %*% beta.hat
pred = as.numeric(pred) # make this a vector instead
# compute prediction RMSE:
sqrt( mean((pred - dat$Salary[-itrain])^2) )
####Exercise 4: fit and predict using stats::step() ####
#使用 R 的基本包 stats 中的函数 step() 对完整的 Hitters 数据集执行逐步选择（一旦删除了缺失值的观察，如练习 2 中所示）。
#1. 比较 step() 和 regsubsets() 的后向选择输出。
#2. 使用 step() 引用通过反向选择选择的最终模型的模型拟合摘要。
#step : 在逐步算法中通过 AIC 选择模型
rm(list=ls())
Hitters = na.omit(Hitters)
dim(Hitters)
# 从 regsubsets() 中逐步选择：
reg.fwd = regsubsets(Salary~., data=Hitters, nvmax=19, method="forward")
reg.bwd = regsubsets(Salary~., data=Hitters, nvmax=19, method="backward")
# 来自 step() 的逐步选择：
lm.out = lm(Salary~., data=Hitters) # full model
step.bth = step(lm.out, direction="both")#默认方法
step.fwd = step(lm.out, direction="forward")
step.bck = step(lm.out, direction="backward")
# compare backward selections from regsubsets() and step():
#
# ... from step()...
coef(step.bck)
length(coef(step.bck))
summary(step.bck)
# Nice: we get the model directly! No need to reconstruct
# fitted values by hand this time!
#
# ... from regsubsets()...
i.opt = which.min(summary(reg.bwd)$bic)
i.set.opt = summary(reg.bwd)$which[i.opt,]
summary(reg.bwd)$which[i.opt, i.set.opt]
# Different models, but smaller one is included in larger one.
# Difference is likely due to using different criteria
# (AIC v BIC, BIC yielding a smaller model).
# NB: we can also assess feature contributions in terms
# of magnitude of their effect:
coefs = abs(coef(step.fwd))/sum(abs(coef(step.fwd)), na.rm=TRUE)*100
coefs = coefs[-1]
coefs[order(abs(coefs), decreasing=TRUE)]
#从大到小每个变量占重要性（1）的比率，后面应该加 %
####Exercise 5: generate predictions from step() output? ####
rm(list=ls())
dat = na.omit(Hitters)
n = nrow(dat)
itrain = sample(1:n, 150)
lmo = lm(Salary~., data=dat, subset=itrain)#这是新添加的
reg.fwd = step(lmo, direction="forward")#这是新添加的
pred = predict(reg.fwd, newdata=dat[-itrain,])
sqrt(mean((pred - dat$Salary[-itrain])^2))
#### 带插入符号的逐步（逻辑）回归 #####
rm(list=ls())
dat = na.omit(Hitters)
n = nrow(dat)
dat = dat[sample(1:n,n),]
dat$y = as.factor(dat$Salary>mean(dat$Salary))#是否大于均值
dat$Salary = NULL
levels(dat$y) = c("low","hi")
# 分层拆分为训练+测试数据集
itrain = createDataPartition(dat$y, p=.75, times=1)[[1]]
dtrain = dat[itrain,]
dtest = dat[-itrain,]
trC = trainControl(method="cv", number=5,
savePredictions = TRUE,
classProbs = TRUE)
co = train(y~., data=dtrain, method='glmStepAIC',
trControl=trC, distribution='binomial')
summary(co$finalModel)
names(co)
preds <- predict(co, dtest)
probs <- predict(co, dtest, type="prob")
table(dtest$y,preds)
pd = data.frame(obs=dtest$y,pred=preds,low=probs$low)
twoClassSummary(pd, lev=levels(dtest$y))
#### on simulated data ####
rm(list=ls())
library(glmnet)
library(leaps)
library(caret)
# We start by creating a dummy data set, so that we know which
# features actually make up the observations. Here a linear
# combination of nonlinear expression of 3 features are used
# to create observations Y.
# The 3 features are:
# - education level
# - number of years of experience
# - some employee rating assessed by their company
# The response variable Y is salary.
# Another 20 features containing random noise are also created
# and added to the dataset. They should not be selected by our
# model selection method...
n = 500 # desired sample size
# level of education (1=secondary level, 2=BSc, 3=MSc, 4=PhD/MBA):
edu = sample(c(1:4), size=n, prob=c(.05,.5,.35,.1), replace=TRUE)
# nbr years experience:
yex = rpois(n=n, lambda=6)
# some obscure employee rating made up by the company:
ert = pmin(5, pmax(0, 5-rexp(n=n, rate=2)))
# employee salary (response variable):
sal = 2*exp(.15*yex) + 3.2*log(edu) + 4*ert
par(mfrow=c(2,2))
plot(factor(edu), main="education")
hist(yex, main="years experience")
hist(ert, main="employee rating")
hist(sal, main="Salaries")
par(mfrow=c(1,3), pch=20)
boxplot(sal~factor(edu), main="salary wrt\n education")
plot(yex, sal, main="salary v\n years experience")
plot(ert, sal, main="salary v\n employee rating")
# now make up some dummy features...
# we don't bother changes scales/means since we will
# be normalizing these features...
p = 20
xtra = matrix(rnorm(n*p), ncol=p, nrow=n)
colnames(xtra) = paste("X",c(1:p),sep="")
par(mfrow=c(4,5), pch=20, mar=c(1,1,1,1))
for(j in 1:p){
plot(xtra[,j], sal, main=paste(j))
}
# the data frame(s):
features = data.frame(edu,yex,ert,xtra)
dat = data.frame(sal,features) # may be more convenient sometimes
# train-test split:
i.train = sample(1:n, size=300, replace=FALSE)
# line up data in several formats for convenience:
dat.train = dat[i.train,]
dat.test = dat[-i.train,]
x.train = features[i.train,]
y.train = sal[i.train]
x.test = features[-i.train,]
y.test = sal[-i.train]
# not forgetting matrix forms for the likes of glmnet:
xm = model.matrix(sal~.,data=features)[,-1]
xm.train = xm[i.train,]
xm.test = xm[-i.train,]
#### check out what LASSO would tell us ####
lasso.cv = cv.glmnet(xm.train, y.train)
lasso = glmnet(xm.train, y.train, lambda=lasso.cv$lambda.min)
coef(lasso)
# c.lasso = caret::train(x.train, y.train, method="glmnet")
# how about cross-validating this?
K = 10
folds = cut(1:n, breaks=K, labels=FALSE)
sel = matrix(0, nrow=K, ncol=ncol(features))
colnames(sel) = names(features)
for(k in 1:K){
itr = which(folds!=k)
lasso.cv = cv.glmnet(xm[itr,], sal[itr])
lasso = glmnet(xm[itr,], sal[itr], lambda=lasso.cv$lambda.min)
isel = which(coef(lasso)[-1] != 0)
sel[k,isel] = 1
}
apply(sel,2,mean)*100
# LASSO thinks X1 and X14, for example, are important...
# We'd be better off increasing the regularization parameter,
# e.g. using lasso.cv$lambda.min*2 instead (try it!).
#### perform FS with caret::rfe based on linear regression ####
subsets <- c(1:5, 10, 15, 20, ncol(features))
ctrl <- rfeControl(functions = lmFuncs,
method = "cv",
number = 10,
# method = "repeatedcv",
# repeats = 5,
verbose = FALSE)
lm.rfe <- rfe(x.train, y.train,
sizes = subsets,
rfeControl = ctrl)
lm.rfe
# This function has picked the correct subset of features
#### compare with leaps...####
reg.bwd = regsubsets(sal~., data=dat.train,
nvmax=ncol(features))
opt.mod = which.max(summary(reg.bwd)$adjr2)
isel = which(summary(reg.bwd)$which[opt.mod,-1])
isel
# how about cross-validating this?
K = 10
folds = cut(1:n, breaks=K, labels=FALSE)
sel = matrix(0, nrow=K, ncol=ncol(features))
colnames(sel) = names(features)
for(k in 1:K){
itr = which(folds!=k)
reg.bwd = regsubsets(sal~., data=dat[itr,],
nvmax=ncol(features))
opt.mod = which.max(summary(reg.bwd)$adjr2)
isel = which(summary(reg.bwd)$which[opt.mod,-1])
sel[k,isel] = 1
}
apply(sel,2,mean)*100
# X1 and X14, again...
#### perform FS with caret::rfe based on RF ####
subsets <- c(1:5, 10, 15, 20, ncol(features))
ctrl <- rfeControl(functions = rfFuncs,
method = "cv",
number = 10,
# method = "repeatedcv",
# repeats = 5,
verbose = FALSE)
rf.rfe <- rfe(x.train, y.train,
sizes = subsets,
rfeControl = ctrl)
rf.rfe
# worth the wait!
# ST4061 / ST6041
# 2021-2022
# Eric Wolsztynski
# ...
#### Section 8: Unsupervised learning Use(s) of PCA...####
# Use(s) of PCA...
#
#主成分分析（Principal Component Analysis，PCA）， 是一种统计方法。只做分类，根据几个X决定Y是哪一类
#通过正交变换将一组可能存在相关性的变量转换为一组线性不相关的变量，转换后的这组变量叫主成分
#k均值聚类算法（k-means clustering algorithm）是一种迭代求解的聚类分析算法，
#其步骤是，预将数据分为K组，则随机选取K个对象作为初始的聚类中心，然后计算每个对象与各个种子聚类中心之间的距离，
#把每个对象分配给距离它最近的聚类中心。聚类中心以及分配给它们的对象就代表一个聚类。每分配一个样本，
#聚类的聚类中心会根据聚类中现有的对象被重新计算。这个过程将不断重复直到满足某个终止条件。
#终止条件可以是没有（或最小数目）对象被重新分配给不同的聚类，没有（或最小数目）聚类中心再发生变化，误差平方和局部最小。
# Section 8: Unsupervised learning
# Use(s) of PCA...
#PCA的原理：don't make decisions based on y but on x's in particular
#PCA的目的：filter out some variables in the original domain.
#PCA的缺点：your features become compulsive features of all input features
#PCA里为什么要scale：
#after scaling，the matrix formed by all x becomes correlation matrix instead of covariance Matrix
#redistribute the information as to maximise the variance
#rearranging data so as to extract most of the information early on.
#pca里的数，及他所占的百分比是怎么来的
#for the matrix formed by all x, every xi has its own enginvalue lambda i,
#the its value is sqrt(lambda i) / sum(sqrt(lambda i))
#因此，我们应该通过标准化来取消数据的大小对其对应的特征值和PCA的影响
#install.packages('fdm2id')
# Section 8: Unsupervised learning
# Use(s) of PCA...
rm(list=ls())
library(caret)
library(pROC)
if(1){
# library(mlbench) #also contains a version of the Ionosphere dataset
# head(Ionosphere)
library(fdm2id)
head(ionosphere)#显示数据的前几行数（不知道有几行）
dat = ionosphere
class(dat[,1])
class(dat[,ncol(dat)])
} else {
require(mlbench)
data(Sonar)
dat = Sonar
}
#要符合PCA的命名逻辑
names(dat) = c(paste("X",1:(ncol(dat)-1),sep=''), "Y")#重命名每列数据，将其变为X,Y
head(dat)
y = dat[,ncol(dat)]
x = dat[,-ncol(dat)]
n = nrow(x)
p = ncol(x)
table(y)/sum(table(y))#看数据里的每种y的占比
apply(x,2,sd)
sapply(x,sd)
sapply(x,mean)
M = cor(x)#correlation
diag(M) = 0
which(abs(M)>.9)
caret::findCorrelation(cor(x))
set.seed(6041)
# itrain = sample(1:n, round(.7*n))
# do stratified sampling instead:分层抽样
itrain = createDataPartition(y, p=.7, times=1)[[1]] #do stratified sampling #做分层抽样#结果和上面那个一样
dtrain = dat[itrain,]
dtest = dat[-itrain,]
# (1) Using logistic regression for prediction...
logo = glm(Y~., dat=dtrain, family='binomial')#想看看数据的logistic regression，没啥用
summary(logo)
logp = predict(logo, newdata=dtest, "response")
summary(logp)
logh = as.factor( levels(y)[(logp>.5)+1] )
# table(logh, dtest$Y)
caret::confusionMatrix(logh, dtest$Y)
roco = pROC::roc(predictor=logp, response=dtest$Y)
roco$auc
ci.auc(roco)# 95% CI of roco$auc
# boxplot(dat$X2~dat$Y)
# (2) Using PCA for prediction...
pca = prcomp(x, scale=TRUE)
plot(pca)
plot(x[,2:3], pch=20)#随便看看两个数据的关系
biplot(pca, col=c(1,2))#有原始数据，有向量
biplot(pca, col=c(0,2))#只有向量，没有原始数据
abline(h=0, v=0, col=8, lty=1)#画坐标轴
j = which(summary(pca)$importance[3,]>.85)[1]####只要PCA里前85%的数据####
names(pca)
xt = pca$x[,1:j]
# create reduced data frame
pca.dat = data.frame(xt,Y=y)
pca.dtrain = pca.dat[itrain,]
pca.dtest = pca.dat[-itrain,]
#把前85%的数据取出来，在做logistics regression
pca.logo = glm(Y~., dat=pca.dtrain, family='binomial')
summary(pca.logo)
pca.logp = predict(pca.logo, newdata=pca.dtest, "response")
pca.logh = as.factor( levels(y)[(pca.logp>.5)+1] )
caret::confusionMatrix(logh, dtest$Y)#PAC前的数据#Accuracy : 0.8173
caret::confusionMatrix(pca.logh, dtest$Y)#Accuracy : 0.7885 #没有变低很多
roco = pROC::roc(predictor=logp, response=dtest$Y)#0.7856
pca.roco = pROC::roc(predictor=pca.logp, response=dtest$Y)#0.8302
roco$auc
pca.roco$auc
ci.auc(roco)
ci.auc(pca.roco)
################################说用caret包，没说干啥，也不分析
set.seed(6041)
trC = trainControl(method="boot", number=10,
# method="cv", number=10,repeat(重复几遍)
savePredictions = "all",
classProbs = TRUE)#bootstrapping 也可以cv
modo = caret::train(x, y, family='binomial', method="glm", trControl=trC)
modo$results
pca.modo = caret::train(xt, y, family='binomial', method="glm", trControl=trC)
pca.modo$results
extract.caret.metrics <- function(co){
# co: caret output
ids = unique(co$pred$Resample)
K = length(ids)
aucs = accs = numeric(K)
for(k in 1:K){
sk = subset(co$pred,Resample==ids[k])
l = co$levels[1]
aucs[k] = roc(response=sk$obs, pred=sk[,l], quiet=TRUE)$auc
tb = table(sk$obs, sk$pred)
accs[k] = sum(diag(tb))/sum(tb)
}
return(list(aucs=aucs, accs=accs))
}
# compare CV AUCs and accuracies:
eo = extract.caret.metrics(modo)
c(mean(eo$aucs), mean(eo$accs))#PCA前AUC和accuracy
pca.eo = extract.caret.metrics(pca.modo)
c(mean(pca.eo$aucs), mean(pca.eo$accs))#PCA后AUC和accuracy
# reporting mean and SE instead:
c(mean(eo$aucs),sd(eo$aucs))#PCA前AUC和accuracy
c(mean(pca.eo$aucs),sd(pca.eo$aucs))#PCA后AUC和accuracy
################################说用caret包，没说干啥，也不分析
# (2) Using PCA to cluster features...聚类
biplot(pca, col=c(0,2))
abline(h=0, v=0, col=8, lty=1)
# reproduce plot of projeted features:
pca.biplot <- function(pca,cols=2,a=1,b=2){
plot(pca$rotation[,c(a,b)],pch='')
abline(h=0,lty=3,col=1,lwd=.5)
abline(v=0,lty=3,col=1,lwd=.5)
nms = row.names(pca$rotation)
text(pca$rotation[,c(a,b)],labels=nms,col=cols)
}
pca.biplot(pca,cols=2,a=1,b=2)#只要向量的点上有向量名字，不要向量线
#loading plot（载荷）
#里面的数表示它作为第i主成分时，其对于平均水平的相对程度，正数表示平均水平以上，负数表示平均水平以下
abline(h=0, v=0, col=8, lty=1)
#如果两个名字太近，don't need them all together，we can cluster on that.聚类
C = 5 #被选作集群的点数 center，也是k
C = 10
x.proj = pca$rotation[,1:2]
#ko = kmeans(pca$rotation[,1:2], centers=5)
# run on first two components 要前两个成分
#take five clusters 采取五个集群
ko = kmeans(x.proj, centers=C)
points(ko$centers, pch=20, cex=3)#看哪C个点被选作集群（选择是随机的）
#how they group up in the projected space, do clustering.
cbind(ko$cluster)#看每个点都被分为了哪个集群
table(ko$cluster)#每个集群都有几个点
# pay attention what you cluster though! 所有PCA都要
ko.all = kmeans(pca$rotation, centers=C)
points(ko.all$centers[,1:2], pch=15, cex=3, col=4)
cbind(ko$cluster, ko.all$cluster) # watch out, cluster numbers are random!
#see the number of clusters OK for each cluster，picking identifying which features are contained in the cluster.
pca.clust.set = numeric(C)
#looking at which picture is the closest to the center
for(ic in 1:C){
fs = which(ko$cluster==ic)
centroid = ko$centers[ic,]
dc = NULL
for(ifs in fs){
dc = c(dc, sqrt(mean((x.proj[ifs,]-centroid)^2)))
}
pca.clust.set[ic] = fs[which.min(dc)]#the closest feature from the centre for each cluster
}
x.ss = x[,pca.clust.set]
set.seed(6041)
pca.cl.modo = caret::train(x.ss, y, family='binomial', method="glm", trControl=trC)
pca.cl.modo$results
pca.cl.eo = extract.caret.metrics(pca.cl.modo)
c(mean(eo$aucs), mean(eo$accs))#auc 和 accuracy
c(mean(pca.eo$aucs), mean(pca.eo$accs))
c(mean(pca.cl.eo$aucs), mean(pca.cl.eo$accs))
# reporting mean and SE instead:
c(mean(eo$aucs),sd(eo$aucs))#AUC及其sd
c(mean(pca.eo$aucs),sd(pca.eo$aucs))
c(mean(pca.cl.eo$aucs),sd(pca.cl.eo$aucs))
#### Another example...in PCA####
library(ISLR)
dim(Hitters)
dat = na.omit(Hitters)
n = nrow(dat)
set.seed(4060)
dat = dat[sample(1:n, n, replace=FALSE),]
i.train = 1:round(.7*n)
dat.train = dat[i.train,]
dat.valid = dat[-i.train,]#test group
# (a) Scaled PCA:
#这里有很多x，这里只选取前6个
set.seed(1)
pca = prcomp(dat.train[,1:6], scale=TRUE)
summary(pca)
# 4 PCs are necessary to capture over 86% of the information
# (b) k-means:
set.seed(1)
ko = kmeans(dat.train[,1:6], 4)#4个集群
table(ko$cluster)
par(font=2, font.axis=2, font.lab=2)
dims = c(1,3) #用x1和x3作图，这里随便谁都行
plot(dat.train[,dims], pch=20, col=c(2,4,3,5)[ko$cluster], cex=2)
#对上面的集群作图
points(ko$centers[,dims], pch='*', col=1, cex=4)
#这里没解释这四个点标出来是干什么的
#根据图感觉应该是每个集群的中心点
#can see clearly that there's 4 groups
#
# From figure:
# Stars indicate the centroids of each cluster
# AtBat contributes more because of scale effect:
# 从图中：
# 星号表示每个簇的质心
# 由于规模效应，AtBat 贡献更大：（AtBat的sd最大）
sapply(dat.train[,1:6],sd)
# (b) k-means:
set.seed(1)
#对PCA进行k-means
pca.ko = kmeans(pca$x, 4)
table(pca.ko$cluster)
par(font=2, font.axis=2, font.lab=2)
dims = c(1,3)
plot(dat.train[,dims], pch=20, col=c(2,4,3,5)[pca.ko$cluster], cex=2)
#和之前的图差别挺大的，也没分析为啥
###1:02讲完了
#### Section 8: miz Imbalance...####
# Imbalance...
#imbalance 就是当数据里绝大部分都是A，只有很少是B时
#比如No占0.9667，而 Yes 只有0.0333
#很多Yes被识别为No
#Sensitivity和Specificity中有一个数会非常小
#处理imbalance（对数据进行加权，让yes的比例变大） 以后
#准确率下降了，但Yes的识别率显著提高
#Sensitivity和Specificity都很大
#
#Confusion Matrix and Statistics
#Reference
#Prediction No Yes
#No TN FN
#Yes FP TP
#True/FALSE
#Positive/Negative
#FP:failed alarm
#FN:false detection
#Sensitivity:TP/(TP+FN)
#Specificity=TN/(TN+FP)
rm(list=ls())
library(ISLR)
head(Default)
tb = table(Default$default)
tb/sum(tb)
dat = Default
set.seed(4061)
itrain = createDataPartition(dat$default, p=.7, times=1)[[1]]
dtrain = dat[itrain,]
dtest = dat[-itrain,]
trC = trainControl(method="boot", number=50,
# method="cv", number=10,
savePredictions = "all",
classProbs = TRUE)
# (1) Using logistic regression for prediction...
set.seed(6041)
x = dtrain
x$default = NULL
y = dtrain$default
modo = caret::train(x, y, family='binomial', method="glm", trControl=trC)
modo$results
preds = predict(modo, dtest)
confusionMatrix(preds, dtest$default)#很多Yes被识别为No
#Sensitivity和Specificity中有一个数会非常小
confusionMatrix(preds, dtest$default, positive=levels(dtest$default)[2])
# positive=levels(dtest$default)[2]： 从positive=yes变成positive=No
#Accuracy和matrix不变，Sensitivity和Specificity数值置换了
levels(dtest$default)
# (2) Using weighted logistic regression for prediction...
tbt = table(dtrain$default)
ws = rev( as.numeric(tbt/sum(tbt)) )
w = numeric(nrow(dtrain))
l = levels(dtrain$default)
w[which(dtrain$default==l[1])] = ws[1]
w[which(dtrain$default==l[2])] = ws[2]
#modo = caret::train(x, y, family='binomial', method="glm", trControl=trC)
modo.w = caret::train(x, y, family='binomial', method="glm", trControl=trC, weights=w)
modo.w$results
preds.w = predict(modo.w, dtest)
confusionMatrix(preds.w, dtest$default, positive=levels(dtest$default)[2])
#准确率下降了，但Yes的识别率显著提高
#Sensitivity和Specificity都很大
